Bolbecker Amanda R, Petersen Isaac T, Kent Jerillyn S, Howell Josselyn M, O'Donnell Brian F, Hetrick William P
Department of Psychological and Brain Sciences, Indiana University , Bloomington, IN , USA.
Front Psychiatry. 2016 Jan 25;7:4. doi: 10.3389/fpsyt.2016.00004. eCollection 2016.
Evidence of cerebellar dysfunction in schizophrenia has mounted over the past several decades, emerging from neuroimaging, neuropathological, and behavioral studies. Consistent with these findings, cerebellar-dependent delay eyeblink conditioning (dEBC) deficits have been identified in schizophrenia. While repeated-measures analysis of variance is traditionally used to analyze dEBC data, hierarchical linear modeling (HLM) more reliably describes change over time by accounting for the dependence in repeated-measures data. This analysis approach is well suited to dEBC data analysis because it has less restrictive assumptions and allows unequal variances. The current study examined dEBC measured with electromyography in a single-cue tone paradigm in an age-matched sample of schizophrenia participants and healthy controls (N = 56 per group) using HLM. Subjects participated in 90 trials (10 blocks) of dEBC, during which a 400 ms tone co-terminated with a 50 ms air puff delivered to the left eye. Each block also contained 1 tone-alone trial. The resulting block averages of dEBC data were fitted to a three-parameter logistic model in HLM, revealing significant differences between schizophrenia and control groups on asymptote and inflection point, but not slope. These findings suggest that while the learning rate is not significantly different compared to controls, associative learning begins to level off later and a lower ultimate level of associative learning is achieved in schizophrenia. Given the large sample size in the present study, HLM may provide a more nuanced and definitive analysis of differences between schizophrenia and controls on dEBC.
在过去几十年里，精神分裂症中小脑功能障碍的证据不断增加，这些证据来自神经影像学、神经病理学和行为学研究。与这些发现一致的是，在精神分裂症中已发现依赖小脑的延迟眨眼条件反射（dEBC）缺陷。虽然传统上使用重复测量方差分析来分析dEBC数据，但分层线性模型（HLM）通过考虑重复测量数据中的依赖性，能更可靠地描述随时间的变化。这种分析方法非常适合dEBC数据分析，因为它的假设限制较少，并且允许方差不相等。本研究使用HLM，在年龄匹配的精神分裂症参与者和健康对照样本（每组N = 56）中，以单线索纯音范式通过肌电图测量dEBC。受试者参加了90次dEBC试验（10个组块），在此期间，400毫秒的纯音与吹向左眼的50毫秒气流同时结束。每个组块还包含1次仅呈现纯音的试验。将所得的dEBC数据组块平均值在HLM中拟合到一个三参数逻辑模型，结果显示精神分裂症组和对照组在渐近线和拐点上存在显著差异，但在斜率上没有差异。这些发现表明，虽然与对照组相比学习率没有显著差异，但在精神分裂症中，联想学习开始趋于平稳的时间较晚，并且联想学习最终达到的水平较低。鉴于本研究中的样本量较大，HLM可能会对精神分裂症患者和对照组在dEBC方面的差异提供更细致和明确的分析。
